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#alphafold

3 posts3 participants0 posts today
Charité Berlin

😮 Charité study analyzes 400 million years of enzyme evolution!

With the aid of #AlphaFold AI, a research team has decoded the laws according to which #enzymes - which catalyze chemical reactions in all living things - evolve. The results, which have now been published in the scientific journal #Nature, are relevant to the development of new antibiotics, for example.

👉 charite.de/en/service/press_re

#CharitéPaper#ScienceMastodon#science
Charité Berlin

😮 Charité-Studie analysiert 400 Millionen Jahre Enzym-Evolution!

Mithilfe der #AlphaFold-KI hat ein Forschungsteam die Gesetzmäßigkeiten entschlüsselt, nach denen sich #Enzyme – die chemische Reaktionen in allen Lebewesen beschleunigen – weiterentwickeln. Die jetzt im Fachmagazin #Nature veröffentlichten Ergebnisse sind beispielsweise für die Entwicklung neuer Antibiotika relevant.

👉 charite.de/service/pressemitte

#CharitéPaper#ScienceMastodon#Forschung
Robyn

Eines der besten Bücher zur organischen Chemie wenn ihr mich fragt. 😍

Brauche ich gerade wieder um mein Wissen über Reaktionsmechanismen aufzufrischen.

Ich mache gerade Proteinfaltung mit #Alphafold und muss dann mit #PyMOL das Substrat in der Tasche des Enzyms bewerten bzw. möchte verstehen, welche Aminosäure genau wo angreift. 😅

Dave nλ=2dsinθ

"AI protein-prediction tool AlphaFold3 is now open source" ... almost.

"Anyone can now download the AlphaFold3 software code and use it non-commercially. But for now, only scientists with an academic affiliation can access the training weights on request"

Stop gatekeeping you priiiiickssss.

#StructuralBiology @strucbio #Alphafold

nature.com/articles/d41586-024

www.nature.comAI protein-prediction tool AlphaFold3 is now open sourceThe code underlying the Nobel-prize-winning tool for modelling protein structures can now be downloaded by academics.
The Japan Times

What’s incredible about the work done across both Baker’s lab and Google DeepMind is the pace of progress. We’re getting closer to a place where the drug-discovery process is more efficient and successful. That’s something to celebrate. japantimes.co.jp/commentary/20 #commentary #worldnews #alphafold #deepmind #google #nobelprize #drugs #medicine #healthcare #ai #chemistry

The Japan Times · Google's DeepMind Nobel Prize showcases AI’s medical potentialBy Lisa Jarvis
Matt Hodgkinson

Juan Mateos-Garcia of Google DeepMind wants equitable use of their tools like AlphaFold. They solved protein structure prediction in 2020 and this is helpful for drug discovery, but researchers in LMIC have barriers to adoption.

Used OpenAlex & PDB to study 20.6K papers citing AlphaFold. LMIC researchers are underrepresented, but study of diseases affecting LMICs is overrepresented - esp. work on natural products.

#ICSSI#ScienceofScience#AItools
Thiago Carvalho

'Determining how 11,269 enzyme structures catalysing 361 different metabolic reactions evolved over 400 million years alongside their molecular functions, we report that metabolism has shaped the structural evolution of enzymes at different levels: the organisms overall metabolism; the topological organisation of the metabolic network; and each enzymes molecular properties.'
#Preprint #Evolution #StructuralBiology #AlphaFold
biorxiv.org/content/10.1101/20

bioRxiv · The Role of Metabolism in Shaping Enzyme Structures Over 400 Million Years of EvolutionThe functions of cells and proteins depend on their biochemical microenvironment. In order to understand how biochemical constraints shaped protein structural evolution, we coupled the extensive genetic and metabolic data from the Saccharomycotina subphylum with the capability of AlphaFold2 to systematically predict structures from sequence. Determining how 11,269 enzyme structures catalysing 361 different metabolic reactions evolved over 400 million years alongside their molecular functions, we report that metabolism has shaped the structural evolution of enzymes at different levels: the organisms overall metabolism; the topological organisation of the metabolic network; and each enzymes molecular properties. For example, structural evolution depends on each enzymes reaction mechanism, on the variability rather than the amount of metabolic flux, and on biosynthetic cost. Evolutionary cost-optimization is stronger on highly abundant enzymes and acts differently on different structural domains, with the exception of small-molecule binding sites, which are prioritised over other structural domains and lack cost-optimisation. Finally, while enzyme surfaces are less constrained, surface residues can also be exposed to positive selection for the co-evolution of protein-protein interaction sites. Accessing AlphaFolds power to predict protein structures systematically and across species barriers, facilitating the integration of protein structures with functional genomics, we were thus able to map biological constraints which shape protein structural evolution at scale and over long timelines. ### Competing Interest Statement MR and AZ are founders and shareholders of Eliptica Ltd. JLS is an advisor for ForensisGroup Inc. AR is a scientific consultant for LifeMine Therapeutics, Inc.
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Dave nλ=2dsinθ

Open letter to Nature editors complaining about the lack of code availability for AF3 (Publishing code is normally a prerequisite for publishing in Nature)

(Yes, I am fully aware of the irony of using a Google form to do this - not my idea, just sharing.)

Boosts Welcome!

#StructuralBiology #Alphafold #Crystallography #CryoEM #NMR @strucbio

docs.google.com/forms/d/e/1FAI

Google DocsLetter to the Editor: AlphaFold3 We are submitting the follow as a Letter to the Editor and will post the text immediately on Zenodo. If you would like to endorse to this letter, please fill out the form below. Authors: Stephanie A. Wankowicz, UCSF Pedro Beltrao, ETH Benjamin Cravatt, Scripps Roland Dunbrack, FCCC Anthony Gitter, UW Madison Kresten Lindorff-Larsen, Copenhagen Sergey Ovchinnikov, MIT Nicholas Polizzi, DFCI/HMS Brian K. Shoichet, UCSF James S. Fraser, UCSF The publication of AlphaFold2 was a breakthrough moment for structural biology. Its impact has been far-ranging. Structure predictions for individual proteins opened new avenues for understanding biological systems and small molecule drug discovery. Large-scale prediction studies enabled evolutionary analyses and genetic variant interpretations. The open code was extended and modified for new methods and applications in protein design and protein-protein assembly prediction. These examples, among many, demonstrate how subsequent research and benchmarks have been made possible because the code and models were open and downloadable. For these reasons, we were disappointed with the lack of code, or even executables accompanying the publication of AlphaFold3 in Nature. Although AlphaFold3 expands AlphaFold2’s capacities to include small molecules, nucleic acids, and chemical modifications, it was released without the means to test and use the software in a high-throughput manner. This does not align with the principles of scientific progress, which rely on the ability of the community to evaluate, use, and build upon existing work. The high-profile publication advertises capabilities that remain locked behind the doors of the parent company. In this publication, several deviations from our community's standards stand out. First, the absence of available code compromises peer review, a cornerstone of scientific publication and a standard typically upheld by journals. Indeed, one of us (RD) was a reviewer, and despite repeated requests, he was not given access to code during the review. Second, the model's limited availability on a hosted web server, capped at ten predictions per day, restricts the scientific community's capacity to verify the broad claims of the findings or apply the predictions on a large scale. Specifically, the inability to make predictions on novel organic molecules akin to chemical probes and drugs, one of the central claims of the paper, makes it impossible to test or use this method. Finally, the pseudocode released will require months of effort to turn into workable code that approximates the performance, wasting valuable time and resources. Even if such a reimplementation is attempted, restricted access raises questions about whether the results could be fully validated. Computational costs of machine learning approaches are becoming prohibitive for academic institutions, owing to the high costs of training the models, leaving much computational research and potential breakthroughs in the hands of for-profit companies. While companies have the right to capitalize on their innovations, using the imprimatur of academic publications without the possibility of reproducing the results, far less building on them, subverts the enterprise. The amount of disclosure in the AlphaFold3 publication is appropriate for an announcement on a company website (which, indeed, the authors used to preview these developments), but it fails to meet the scientific community’s standards of being usable, scalable, and transparent. This moment can motivate our community to raise the bar of openness and transparency to accelerate scientific progress. When journals fail to enforce their written policies about making code available to reviewers1 and alongside publications2, they demonstrate how these policies are applied inequitably and how editorial decisions do not align with the needs of the scientific community. While there is an ever-changing landscape of how science is performed and communicated, journals should uphold their role in the community by ensuring that science is reproducible upon dissemination, regardless of who the authors are. AI approaches now directly impact biological discovery and human health. Fully realizing their potential will require not only technical breakthroughs but also open and collaborative efforts to build on others’ findings, ​​as is foundational in all scientific research. 1)https://web.archive.org/web/20240511023627/https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards 2)https://web.archive.org/web/20240511023855/https://www.nature.com/nature/for-authors/initial-submission
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